Economic modeling for improved prediction of saving estimates in healthcare costs from consumption of healthy foods: the Mediterranean-style diet case study
Bibliographic record
Abstract
BACKGROUND: By design, existing scenario-based nutrition economics studies on the financial benefits of healthy dietary behaviors generally report uncertainty in inputs and wide ranges of outcome estimates. OBJECTIVES: This modeling exercise aimed to establish precision in prediction of the potential healthcare cost savings that would follow a reduction in the incidence of cardiovascular disease (CVD) consistent with an increase in adherence to a Mediterranean-style diet (MedDiet). DESIGN: simulation model on a cost-of-illness analysis assessing MedDiet adherence, CVD incidence reduction, and healthcare cost savings in the United States and Canada, short- and long-term cost savings that are likely to accrue to the American and Canadian healthcare systems were estimated using 20 and 80% increases in MedDiet adherence scenarios. RESULTS: Increasing percentage of population adhering to a MedDiet by 20% beyond the current adherence level produced annual savings in CVD-related costs of US$8.2 billion (95% confidence interval [CI], $7.5-$8.8 billion) in the United States and Can$0.32 billion (95% CI, $0.29-$0.34 billion) in Canada. An 80% increase in adherence resulted in savings equal to US$31 billion (95% CI, $28.6-$33.3 billion) and Can$1.2 billion (95% CI, $1.11-$1.30 billion) in each respective country. CONCLUSION: simulation, could be an effective way of incorporating variability of modeling parameters in nutrition economics studies for improved precision in estimating the monetary value of healthy eating habits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".